Mobile communications are subject to adverse ambient noise conditions. A user listening to a signal received over a communication channel perceives the quality of the signal as being degraded as a result of the ambient noise at both the transmitting end of the communication channel (far-end) and the ambient noise at the user's receiving end of the communication channel (near-end).
The problem of far-end ambient noise has been extensively addressed through the application of noise reduction algorithms to signals prior to their transmission over a communication channel. These algorithms generally lead to far-end ambient noise being well compensated for in signals received at a user apparatus, such that the fact that a far-end user may be located in a noisy environment does not significantly disrupt a near-end user's listening experience.
The problem of near-end ambient noise has been less well addressed. Near-end ambient noise often has the effect of masking a speech signal such that the speech signal is not intelligible to the near-end listener. The conventional method of improving the intelligibility of speech in such a situation is to apply an equal gain across all frequencies of the received speech signal to increase its total power. However, increasing the power across all frequencies can cause discomfort and listening fatigue to the listener. Additionally, the digital dynamic range of the signal processor in the user apparatus limits the amplification that can be applied to the signal, with the result that clipping of the signal may occur if a sufficiently high gain factor is applied.
There is therefore a need to provide a user apparatus capable of improving the perceived intelligibility of a speech signal as determined by a listener at the user apparatus when the user apparatus is located in a region of significant ambient noise.
A separate problem to that of near-end ambient noise is the problem of the narrow bandwidth of signals received over a telephony channel. Telephony channels have a limited bandwidth of 0.3 kHz to 3.4 kHz. Speech signals are truncated from their original wideband form to a narrowband form such that they can be transmitted in the available bandwidth of the telephony channel. The absence of speech in frequency bands higher than 3.4 kHz reduces the perceived quality of speech signals. Consequently, it is desirable to extend the effective bandwidth of a received narrowband speech signal to the equivalent of the original wideband signal, for example from 0 kHz up to 8 kHz.
Bandwidth extension techniques have been proposed which reconstruct wideband signals using statistical speech models. For example the Gaussian Mixture model (GMM) can be used to reconstruct a wideband spectrum envelope from a narrowband speech signal, and speech can then be generated for the wideband signal using the reconstructed spectral envelope and linear predictive coding (LPC).
Such techniques are computationally complex and are therefore undesirable for use with low-power platforms.
A further problem with bandwidth extension techniques is that they tend to over-estimate the power of the extended signal, thereby introducing undesirable artefacts in the speech signal which are audible to the listener. An approach has been suggested to control the shape of the extended signal using a confidence controlled bandwidth extension algorithm. This algorithm uses an asymmetric cost-function that penalises over-estimates of the energy in the extended band more than under-estimates. However, this technique is computationally complex and therefore undesirable for use with low-power platforms.
There is therefore a need for a low complexity bandwidth extension method.